CN112069930A - Vibration signal processing method and device for improving GIS equipment fault diagnosis accuracy - Google Patents
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Abstract
The invention discloses a vibration signal processing method and a device for improving the fault diagnosis accuracy of GIS equipment, wherein the method comprises the steps of collecting vibration signals of the GIS equipment under different health levels; dividing a GIS equipment vibration signal into a plurality of samples by taking one period as a time length, and constructing a vibration signal data set; performing normalization processing on all samples in the vibration signal data set, and then performing one-dimensional to two-dimensional imaging operation to obtain imaged vibration signals to obtain a vibration image data set; dividing the vibration image data set into a training set and a test set according to a preset proportion, and constructing a GIS equipment fault diagnosis model based on a convolutional neural network; and normalizing the GIS equipment vibration signals collected in real time, and then performing imaging operation to obtain imaged vibration signals, inputting the imaged vibration signals into a GIS equipment fault diagnosis model to obtain the health grade of the current GIS equipment, thereby realizing GIS equipment fault diagnosis. The invention judges the specific running state of the GIS equipment and has higher fault diagnosis accuracy.
Description
Technical Field
The invention belongs to the technical field of state monitoring and fault diagnosis of GIS equipment, and particularly relates to a vibration signal processing method and device for improving the fault diagnosis accuracy of the GIS equipment.
Background
The past research shows that most serious accidents in the power system are caused by equipment faults, and in recent years, the equipment quantity of Gas Insulated Switchgear (GIS) equipment is gradually and rapidly increased, and the reliability of the GIS equipment is related to the safe operation of a power grid. The method is significant for a power system by researching how to model the GIS equipment fault so as to diagnose the fault timely and accurately.
GIS equipment is indispensable equipment in present power systems, and is widely used in the high-voltage and ultra-high-voltage fields. The equipment is characterized in that parts such as a circuit breaker, a disconnecting switch, an earthing switch, a mutual inductor, a lightning arrester, a bus, a connecting piece, an outgoing line terminal and the like are sealed in a metal grounded shell, and sulfur hexafluoride (SF) with certain pressure is filled in the metal grounded shell6) An insulating gas. The failure of the GIS equipment can be divided into two major types, i.e., a discharge failure and a mechanical failure. At present, the research methods for GIS equipment discharge faults mainly include a pulse current method, an ultrahigh frequency method and a gas decomposition method, and the related research for mechanical faults is still in a starting stage, and the main method is a vibration analysis method. The pulse current method is to determine the discharge capacity of the equipment by measuring the voltage variation in the circuit, so as to judge the running state of the GIS equipment, but the diagnosis precision of the method is not high due to the electromagnetic pulse interference existing in the GIS equipment; the ultrahigh frequency method is used for detecting signals in a GHz frequency band so as to judge the running state of the GIS equipment, but the method is difficult to accurately judge the partial discharge state; the gas decomposition method is based on SF6The running state of the GIS equipment is judged by the decomposition product of the gas, but the method is only used when the maintenance power is cut off, and the running state of the equipment cannot be monitored in real time. Meanwhile, state network companies release a plurality of intelligent instructional documents for high-voltage equipment, wherein state monitoring and fault diagnosis are regarded as key functions and difficulties of intelligent electrical appliances. Therefore, it is necessary to develop a mechanical state detection technology and a diagnosis method for the GIS equipment to find latent mechanical defects inside the equipment in time and ensure safe and stable operation of the equipment.
The latent danger of the GIS device is difficult to accurately diagnose through the conventional electrical characteristic parameters. The vibration signal of the GIS equipment is state information which is easy to measure and can reflect rich dynamic information of the health state of the GIS equipment. However, acquiring only the vibration signal from the GIS device is not enough to solve the problem of fault diagnosis, and many subsequent processes are required to be performed on the vibration signal and then a model is established to achieve the purpose of fault identification. Many models established by traditional machine learning methods have appeared based on vibration signals, however, most of the methods have certain limitations, and great breakthrough cannot be made in performance. The convolutional neural network adopts a deep system algorithm structure, and can learn multi-level data representation corresponding to different abstract levels. Convolutional neural networks have made great progress in two-dimensional data scenes such as computer vision, but there is no mature model on one-dimensional vibration signals.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a vibration signal processing method and a vibration signal processing device for improving the fault diagnosis accuracy of GIS equipment, and aims to solve the problems of difficulty in manual signal feature extraction and low identification quality.
In order to achieve the above object, according to an aspect of the present invention, there is provided a vibration signal processing method for improving accuracy of a fault diagnosis of a GIS device, including the steps of:
step 1, dividing the GIS equipment into different health levels according to the running conditions of the GIS equipment, and collecting vibration signals of the GIS equipment under the different health levels;
step 2, dividing the GIS equipment vibration signal into a plurality of samples by taking an electromagnetic force period inside the GIS equipment as a time length, and constructing a vibration signal data set;
step 3, performing normalization processing on all samples in the vibration signal data set, and then performing one-dimensional to two-dimensional imaging operation on the samples after the normalization processing to obtain imaged vibration signals and obtain a vibration image data set;
step 4, dividing the vibration image data set into a training set and a testing set according to a preset proportion, and constructing a GIS equipment fault diagnosis model based on a convolutional neural network;
and 5, carrying out imaging operation after normalizing the GIS equipment vibration signals collected in real time, inputting the obtained imaged vibration signals into a GIS equipment fault diagnosis model, obtaining the health grade of the current GIS equipment, and realizing GIS equipment fault diagnosis.
Further, the normalization process in step 3 can be formulated as:
wherein X is X1，...，xKIs the original sample, x, in the vibration signal data setiValues representing sample points, max (x)i) Represents the maximum value in the sample point, min (x)i) Represents the minimum value of the sampling points, and K is the number of sampling points of the sample.
Further, the imaging operation in step 3 specifically includes:
step 3.1, the normalized vibration signal dataset samples are represented as
Mapping the vibration signal data set from a rectangular coordinate system to a polar coordinate system:
wherein K is the number of sampling points of the sample, i represents the ith sampling point,
is the polar angle, riIs the pole diameter;
step 3.2, define an inner product operation, using symbols
The mathematical description of which is shown in the following formula;
step 3.3, carrying out vibration signal data set under a polar coordinate system
And (3) operation to obtain a Gram-like matrix G:
and 3.4, converting the elements in the matrix G into pixel values, and arranging according to the positions in the matrix to obtain a vibration image data set.
Further, the training of the convolutional neural network-based GIS device fault diagnosis model in step 4 includes:
step 4.1, building a convolutional neural network, taking a vibration image training set as input, and outputting the vibration image training set as the health grade of the GIS equipment;
step 4.2, training a GIS equipment fault diagnosis model based on the convolutional neural network, and selecting cross entropy as a loss function of training;
and 4.3, testing the GIS equipment fault diagnosis model based on the convolutional neural network, inputting the vibration image test set into the trained convolutional neural network model to obtain a predicted health grade, then comparing the predicted health grade with the real health grade, and calculating the prediction accuracy rate which is used for evaluating the precision of the model.
The research object of the method is the vibration signal acquired by the external shell of the breaker when the GIS equipment runs, the mechanical fault of the GIS equipment can be efficiently diagnosed in real time, and the accuracy rate is effectively improved compared with the traditional fault diagnosis method by using the vibration signal. Based on the above problems, we have studied a method for diagnosing equipment faults based on imaging vibration signals, which can convert one-dimensional vibration signals into two-dimensional images without losing original features, and in this way, can not only expand signal features but also fully utilize a plurality of excellent models (such as convolutional neural network models) in the image recognition field. The GIS equipment fault diagnosis method based on the imaging vibration signals effectively solves the problems of difficulty in manual signal feature extraction and low identification quality.
According to another aspect of the present invention, there is provided a vibration signal processing apparatus for improving accuracy of a failure diagnosis of a GIS device, comprising:
the vibration signal acquisition module is used for dividing the GIS equipment into different health grades according to the running conditions of the GIS equipment and acquiring vibration signals of the GIS equipment under the different health grades;
the vibration signal construction module is used for dividing the GIS equipment vibration signal into a plurality of samples by taking the electromagnetic force period inside one GIS equipment as the time length and constructing a vibration signal data set;
the vibration image acquisition module is used for carrying out normalization processing on all samples in the vibration signal data set and then carrying out one-dimensional to two-dimensional imaging operation on the samples after the normalization processing to obtain imaged vibration signals and obtain a vibration image data set;
the diagnostic model building module is used for dividing the vibration image data set into a training set and a testing set according to a preset proportion and building a GIS equipment fault diagnostic model based on a convolutional neural network;
and the fault diagnosis module is used for carrying out imaging operation after normalizing the GIS equipment vibration signals collected in real time, inputting the obtained imaged vibration signals into a GIS equipment fault diagnosis model, obtaining the health grade of the current GIS equipment and realizing GIS equipment fault diagnosis.
Further, the normalization process can be formulated as:
wherein X is X1，...，xKIs the original sample, x, in the vibration signal data setiValues representing sample points, max (x)i) Representing maximum in sample pointValue, min (x)i) Represents the minimum value of the sampling points, and K is the number of sampling points of the sample.
Further, the imaging operation specifically includes:
the normalized vibration signal dataset samples are represented as
Mapping the vibration signal data set from a rectangular coordinate system to a polar coordinate system:
wherein K is the number of sampling points of the sample, i represents the ith sampling point,
is the polar angle, riIs the pole diameter;
defining an inner product operation using symbols
The mathematical description of which is shown in the following formula;
carrying out vibration signal data set under a polar coordinate system
And (3) operation to obtain a Gram-like matrix G:
the elements in the matrix G are converted into pixel values and arranged according to the positions in the matrix, resulting in a vibration image dataset.
Compared with the prior art, the GIS equipment fault diagnosis method based on the imaging vibration signals and the convolutional neural network overcomes the limitations of the traditional fault diagnosis method in real time and accuracy. According to the method, firstly, the collected vibration signals of the outer shell of the breaker when the GIS equipment runs are subjected to normalization processing, then the vibration signals are processed by adopting imaging operation, and the imaged vibration signals are obtained. And then, the convolutional neural network is utilized to identify the imaging vibration signals of the GIS equipment in different operation states, the specific operation state of the GIS equipment can be judged, the fault diagnosis accuracy is higher, and the GIS equipment can also perform well under different working conditions and noise-containing environments.
Drawings
FIG. 1 is a schematic flow chart of a vibration signal processing method for improving the GIS equipment fault diagnosis accuracy rate provided by the invention;
FIG. 2 is a flow chart of a specific implementation of the vibration signal imaging operation provided by the present invention;
fig. 3 is a schematic structural diagram of a convolutional neural network provided by the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
A GIS equipment fault diagnosis method based on an imaging vibration signal and a convolutional neural network is disclosed, as shown in FIG. 1, and comprises the following steps:
step 1, dividing the health state of GIS equipment into four health levels of poor health level, attention level, good health level and excellent health level according to the running state of the GIS equipment, and collecting GIS equipment vibration signals under different health levels;
step 2, dividing the collected GIS equipment vibration signal into a plurality of samples by taking an electromagnetic force period inside the GIS equipment as a time length, wherein the length of each sample is 400 sampling points, dividing the samples with the same quantity and different health levels, and constructing a vibration signal data set;
step 3, performing normalization processing on all samples in the vibration signal data set, and then performing one-dimensional to two-dimensional imaging operation on the samples after the normalization processing to obtain imaged vibration signals and obtain a vibration image data set;
and 4, performing 7: 3, dividing the proportion into a training set and a test set, and constructing a GIS equipment fault diagnosis model based on the convolutional neural network, wherein the training set is used for training the GIS equipment fault diagnosis model based on the convolutional neural network, and the test set is used for testing the classification accuracy of the GIS equipment fault diagnosis model;
and 5, carrying out imaging operation after normalizing the GIS equipment vibration signals collected in real time, inputting the obtained imaged vibration signals into a GIS equipment fault diagnosis model, obtaining the health grade of the current GIS equipment, and realizing GIS equipment fault diagnosis.
Further, the pretreatment in the step 1 is normalization treatment,
the calculation formula is as follows:
wherein X is X1，...，xKIs the original sample, x, in the vibration signal data setiValues representing sample points, max (x)i) Represents the maximum value in the sample point, min (x)i) Represents the minimum value of the sampling points, and K is the number of sampling points of the sample.
Further, the imaging operation in step 3 is shown in fig. 2, and specifically includes the following sub-steps:
step 3.1, assume that the normalized vibration signal dataset samples are represented as
Wherein K is the number of sampling points of the sample, the subscript is taken as an abscissa,
the value of (d), i.e. the amplitude, is taken as the ordinate. The vibration signal data set is then mapped from a rectangular coordinate system to a polar coordinate system:
wherein K is the number of sampling points of the sample, i represents the ith sampling point,
is the polar angle, riIs the pole diameter.
Step 3.2, a novel inner product operation is defined, and symbols are used
To show that the novel inner product can make full use of the information from two sampling points, and the mathematical description is shown as the following formula;
step 3.3, carrying out novel inner product operation on the new sequence coded by the polar coordinates to obtain a Gram-like matrix;
and 3.4, multiplying all the elements in the matrix G by 256 to convert the elements into pixel values, and arranging the pixel values according to the positions in the matrix to obtain a vibration image data set.
Further, the training of the convolutional neural network-based GIS device fault diagnosis model in step 4 includes:
and 4.1, building a convolutional neural network, taking the vibration image training set as input, and outputting the vibration image training set as the health grade of the GIS equipment. The basic hierarchical structure of the convolutional neural network is an input layer for data input, a convolutional layer for feature extraction, a ReLu function excitation layer, a pooling layer for feature selection, and a full-link layer for classifying features, and the structure is shown in fig. 3. The convolutional layer is set to be 3 layers, the pooling layer is set to be 4 layers, and the number of fully connected neurons is set to be the number of GIS equipment health grade levels. The hyper-parameters in the convolutional neural network are set, the convolutional kernel size in the convolutional layers is set to 3 x 3, the convolutional kernel size in the pooling layers is set to 2 x 2, the number of convolutional kernels in the first convolutional layer is set to 8, the number of convolutional kernels in the second convolutional layer is set to 16, and the number of convolutional kernels in the third convolutional layer is set to 32.
And 4.2, training a GIS equipment fault diagnosis model based on the convolutional neural network. The cross entropy is selected as a loss function of training, the network learning rate is set to be 0.001, an Adam optimizer is used for updating network parameters, the batch size is set to be 64, and 200 rounds of training are performed in total.
And 4.3, testing the GIS equipment fault diagnosis model based on the convolutional neural network. And inputting the vibration image test set into the trained convolutional neural network model to obtain the predicted health grade. The predicted health grade is then compared to the true health grade, and the prediction accuracy is calculated, which is used to evaluate the accuracy of the model.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (7)
1. A vibration signal processing method for improving the fault diagnosis accuracy of GIS equipment is characterized by comprising the following steps:
step 1, dividing the GIS equipment into different health levels according to the running conditions of the GIS equipment, and collecting vibration signals of the GIS equipment under the different health levels;
step 2, dividing the GIS equipment vibration signal into a plurality of samples by taking an electromagnetic force period inside the GIS equipment as a time length, and constructing a vibration signal data set;
step 3, performing normalization processing on all samples in the vibration signal data set, and then performing one-dimensional to two-dimensional imaging operation on the samples after the normalization processing to obtain imaged vibration signals and obtain a vibration image data set;
step 4, dividing the vibration image data set into a training set and a testing set according to a preset proportion, and constructing a GIS equipment fault diagnosis model based on a convolutional neural network;
and 5, carrying out imaging operation after normalizing the GIS equipment vibration signals collected in real time, inputting the obtained imaged vibration signals into a GIS equipment fault diagnosis model, obtaining the health grade of the current GIS equipment, and realizing GIS equipment fault diagnosis.
2. The vibration signal processing method according to claim 1, wherein the normalization processing in step 3 can be formulated as:
wherein X is X1，...，xKIs the original sample, x, in the vibration signal data setiValues representing sample points, max (x)i) Represents the maximum value in the sample point, min (x)i) Representing the minimum of the samples, K being the sample pointAnd (4) the number.
3. The vibration signal processing method according to claim 1, wherein the imaging operation in step 3 specifically includes:
step 3.1, the normalized vibration signal dataset samples are represented as
Mapping the vibration signal data set from a rectangular coordinate system to a polar coordinate system:
wherein K is the number of sampling points of the sample, i represents the ith sampling point,
is the polar angle, riIs the pole diameter;
step 3.2, define an inner product operation, using symbols
The mathematical description of which is shown in the following formula;
step 3.3, carrying out vibration signal data set under a polar coordinate system
And (3) operation to obtain a Gram-like matrix G:
and 3.4, converting the elements in the matrix G into pixel values, and arranging according to the positions in the matrix to obtain a vibration image data set.
4. The vibration signal processing method according to claim 1, wherein the training of the convolutional neural network-based GIS device fault diagnosis model in step 4 comprises:
step 4.1, building a convolutional neural network, taking a vibration image training set as input, and outputting the vibration image training set as the health grade of the GIS equipment;
step 4.2, training a GIS equipment fault diagnosis model based on the convolutional neural network, and selecting cross entropy as a loss function of training;
and 4.3, testing the GIS equipment fault diagnosis model based on the convolutional neural network, inputting the vibration image test set into the trained convolutional neural network model to obtain a predicted health grade, then comparing the predicted health grade with the real health grade, and calculating the prediction accuracy rate which is used for evaluating the precision of the model.
5. The utility model provides a promote vibration signal processing apparatus of GIS equipment failure diagnosis rate of accuracy which characterized in that includes:
the vibration signal acquisition module is used for dividing the GIS equipment into different health grades according to the running conditions of the GIS equipment and acquiring vibration signals of the GIS equipment under the different health grades;
the vibration signal construction module is used for dividing the GIS equipment vibration signal into a plurality of samples by taking an electromagnetic force period inside one GIS equipment as a time length and constructing a vibration signal data set;
the vibration image acquisition module is used for carrying out normalization processing on all samples in the vibration signal data set and then carrying out one-dimensional to two-dimensional imaging operation on the samples after the normalization processing to obtain imaged vibration signals and obtain a vibration image data set;
the diagnostic model building module is used for dividing the vibration image data set into a training set and a test set according to a preset proportion and building a GIS equipment fault diagnostic model based on a convolutional neural network;
and the fault diagnosis module is used for carrying out imaging operation after normalizing the GIS equipment vibration signals collected in real time, inputting the obtained imaged vibration signals into a GIS equipment fault diagnosis model, obtaining the health grade of the current GIS equipment and realizing GIS equipment fault diagnosis.
6. The vibration signal processing apparatus according to claim 5, wherein the normalization process is formulated as:
wherein X is X1，...，xKIs the original sample, x, in the vibration signal data setiValues representing sample points, max (x)i) Represents the maximum value in the sample point, min (x)i) Represents the minimum value of the sampling points, and K is the number of sampling points of the sample.
7. The vibration signal processing apparatus according to claim 5, wherein the imaging operation specifically includes:
the normalized vibration signal dataset samples are represented as
Mapping the vibration signal data set from a rectangular coordinate system to a polar coordinate system:
wherein K is the number of sampling points of the sample, i represents the ith sampling point,
is the polar angle, riIs the pole diameter;
defining an inner product operation using symbols
The mathematical description of which is shown in the following formula;
and (3) carrying out ^ operation on the vibration signal data set under the polar coordinate system to obtain a Gram-like matrix G:
the elements in the matrix G are converted into pixel values and arranged according to the positions in the matrix, resulting in a vibration image dataset.
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| CN112883639A (en) * | 2021-02-03 | 2021-06-01 | 国网浙江省电力有限公司宁波供电公司 | GIS equipment service life prediction device and method based on machine learning |
| CN113191247A (en) * | 2021-04-27 | 2021-07-30 | 国网山西省电力公司电力科学研究院 | GIS equipment mechanical fault diagnosis method and system |
| CN113295702A (en) * | 2021-05-20 | 2021-08-24 | 国网山东省电力公司枣庄供电公司 | Electrical equipment fault diagnosis model training method and electrical equipment fault diagnosis method |
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